Cognitive systems

Today, there are more data multipliers than ever before — including humans, machines, and business processes – and the volume of structured and unstructured data (social media posts, documents, emails, images, videos, audio recordings, customer feedback, manuals, industry reports etc.) is growing exponentially. Bell.One™ Cognitive Systems is an intelligent service for business data analysis and visualization that allows users to independently and quickly identify the patterns and meaning of data. Cognitive computing is based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks in an intelligent way. Due to the guided analysis of data structure, automatic predictive analytics and cognitive abilities, users can work with data in a natural language and receive comprehensible answers.

Key Features

  • Probabilistic. Bell.One™ Cognitive systems deliver confidence-weighted responses with supporting evidence.
  • Adaptive. The cognitive systems reflect the ability to adapt to any environment. They are dynamic in data gathering and understanding goals and requirements.
  • Interactive. The cognitive systems are able to interact easily with users so that users can define their needs comfortably.
  • Iterative & Stateful. Application of the data quality and validation methodologies ensures that cognitive systems are always provided with enough information and that the data sources they operate on deliver reliable and up-to-date input.
  • Contextual. Ability to understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal from both structured and unstructured digital information sources.

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Benefits

  • Increased cost-effectiveness and continuous improvement of the company's performance over time
  • Improved decision-making and risk evaluation processes
  • Reduced time between data acquisition and actionable insight
  • More expertise applied to problem-solving
  • Deeper engagement between man and machine
  • Reduced human bias by applying evidence-based processes